Examples with linear time series processes. We assess the quality of the results with SNPE-C via the Wasserstein distance [1]} calculated over samples from the analytic and approximated posteriors of bimodal-AR(2) and MA(2). We compare the results obtained when the summary features are extracted via the YuleNet architecture to when they are estimates of the autocorrelations of the time series ( autocorr). Note that because \(u\)  is a Gaussian white noise in both (REF ) and (REF ), the autocorrelations are sufficient statistics, making them the ideal benchmark. We also consider a case where \(f_{\lambda }\)  is a PEN with the same setup from [2]} in two situations: when its parameters are learned within the SNPE-C procedure (PEN), and when they are learned separately on \(10^4\)  simulated samples and then applied to a rejection-sample ABC procedure (PEN+ABC) as done in [2]}.
